ALPHABET-CONSTRAINED AND ENTROPY-CONSTRAINED VECTOR QUANTIZATION OF IMAGE PYRAMIDS

A recently introduced algorithm for multirate vector quantization is used for coding image pyramids. The algorithm, called alphabet- and entropy-constrained vector quantization (AECVQ), operates by optimally choosing sub-codebooks from a large generic codebook. Simulations using 1-D AR and speech samples and full-band image data have shown the performance of AECVQ to be equal to that of entropy-constrained VQ (ECVQ); however, the ECVQ, which is also the best existing vector quantizer, is a single-rate coder. Excellent results at 1 bpp and below, judged both visually and using peak-to-peak SNR criterion, have been obtained by coding image pyramids using the AECVQ algorithm. These results demonstrate significant improvements over existing schemes. Although an AECVQ-based image coding scheme is considerably complex, it can be implemented in real time using current VLSI technology.